Improving VLAD with regional PCA whitening

In recent yeas, VLAD has been used to represent an image effectively and efficiently by just a few bytes in large-scale image retrieval. In spite of its remarkable performance, a series of modification methods have been presented. In addition, the redundancy between the features corresponding to the same cluster center could be improved. In this paper, a regional PCA Whitening method is proposed to decorrelate the features and reduce the dimensionality for each cluster with the consideration of mapping the descriptor into high dimensionality explicitly. Our method can also be embedded into original VLAD pipeline with global PCA very well. The experimental results on both Holidays and UKbench dataset show that our approach improves VLAD significantly.